Air quality in Africa has become a major public health issue. Initial assessments conducted in large African cities show that fine particle concentrations exceed the thresholds recommended by the World Health Organization (WHO). We have developed a mobile device using the Internet of Things (IoT) to monitor particulate pollution levels (PM10, PM2.5), as well as other indicators. The key innovation of this project lies in the integration of a microcontroller (ESP8266 nodeMCU) that not only collects the data measured by the sensors but also transmits them to a server in real-time via Wi-Fi connectivity and the HTTP protocol. This device was installed on a vehicle for 1 month as part of an intensive measurement campaign conducted in the Dakar region of Senegal. The results obtained made it possible to identify the most polluted areas of the city (hot spots) and the peak pollution hours during the day.

This study aims to optimize daily forecasts of the PM2.5 concentrations in Dakar, Senegal using a long short-term memory (LSTM) neural network model. Particulate matter, aggravated by factors such as dust, traffic, and industrialization, poses a serious threat to public health, especially in developing countries. Existing models such as the Autoregressive integrated moving average (ARIMA) have limitations in capturing nonlinear relationships and complex dynamics in environmental data. Using four years of daily data collected at the Bel Air station, this study shows that the LSTM neural network model provides more accurate forecasts with a root mean square error (RMSE) of 3.2 μg/m³, whereas the RMSE for ARIMA is about 6.8 μg/m³. The LSTM model predicts reliably up to 7 days in advance, accurately reproducing extreme values, especially during dust event outbreaks and peak travel periods. Computational analysis shows that using Graphical Processing Unit and Tensor Processing Unit processors significantly reduce the execution time, improving the model efficiency while maintaining high accuracy. Overall, these results highlight the usefulness of the LSTM network for air quality prediction and its potential for public health management in Dakar.

The transition to renewable and sustainable energy sources is critical to solving the environmental and socioeconomic problems associated with the use of fossil fuels. This study uses an interdisciplinary approach to analyze the challenges and prospects of a sustainable energy transition in contexts with the recent discovery and exploitation of fossil resources. We study the case of Senegal from 2000 to 2027 and the role of recent discoveries of natural gas in its energy transition. In 2000, Senegal’s energy mix consisted of about 97% fossil energy and only 3% renewable energy. Since then, the country has developed renewable energy sources, including solar, hydro, and wind power, which currently account for about 30% of the total energy mix. At the same time, Senegal’s population and electricity production have grown significantly, leading to a fivefold increase in per capita energy consumption over the past two decades. Projections based on a long short-term memory model that predicts future electricity demand and energy balance suggest a structural shift in the energy mix, with natural gas, oil, and renewables at 47%, 32%, and 21%, respectively, by 2027. Overall, this study presents a comprehensive analysis that highlights the benefits of strategically using natural gas as a transition energy source in contexts with increased electricity demand and continued development of renewable energy sources.

The objective of this work is to predict daily PM2.5 air quality in Dakar, Senegal using data from an automated measurement station integrated into a server using a data assimilation model. Initially, a 3-year data set was used to identify and validate an appropriate ARIMA data assimilation model. The data was split into an 80% training set and a 20% test set. The Augmented Dickey-Fuller (ADF) test was used to check the normality of the data series. Subsequently, we used the AutoArima method to determine the optimal model to represent the time series. Preliminary results show that a model with order (2,1,1) accurately represents the series. Additional analysis using model fit tests showed that the (3, 0, 1) model was most effective in representing and predicting the data. The statistical validation performance of this model demonstrates its capability to forecast PM2.5 concentrations for up to 72 hours (3 days), achieving correlation coefficients exceeding 80%. However, after three days, the predictions returned to background levels. In the final stage of the study, data from automatic stations were integrated into a server hosting the assimilation model to improve daily PM2.5 forecasts for Dakar. An interactive platform was developed to visualize measurements and forecasts over two days. The results show that by integrating the data with the assimilation model, predictions are significantly improved.